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Some Thoughts on the Impact of Machine Learning on the Banking Industry – Beyond Myths

In 1890, a company was founded in the USA that eventually developed into a leading brand. 80 years after its inception, the company had a market share of approximately 90% in its core field of business: film and photography. The company’s name is Kodak. Kodak was known for its top performance and the forward-looking view of its engineers. These engineers invented the digital camera in 1975. In spite of its long and successful company history, Kodak went bankrupt in 2012. The opportunities provided by the company’s breakthrough technology and its engineering prowess may not have been fully leveraged by the management. Kodak may provide a useful lesson and an incentive for leaders to manage and leverage disruptive technologies.
From today’s view, we believe that machine learning and deep learning are such disruptive technologies that should be leveraged. Advanced computing methods have become more and more popular alongside advances in the emergence of big data. In the finance and banking sector, increasing demand for more efficient approaches has been seen especially in the field of machine learning. The field was originally devoted to developing algorithms in artificial intelligence. In response to its significant advances, however, machine learning and its subfield of deep learning have emerged as breakthroughs with vast applications in a wide array of fields.
In our paper, we discuss the existing definitions as well as our thoughts on some of the applications of machine learning and deep learning. We will provide an insight into the significance of artificial intelligence in the banking industry, in particular and its response to big data market forces, with a focus on German Banks. To illustrate the breadth of the field’s significance, we will also address the automotive industry in Germany and its connection to machine learning technologies. To deepen our understanding of the subject, we will explain the mathematical background behind deep learning and its intersection with artificial neural networks. We will provide a brief look into the subject of Blockchain and its relevance to artificial intelligence. A second part of this paper will analyze the practical implications (use cases) of the technologies and their expected developments in the short and long term.
[Authors: Farhad Khakzad, Sangmeng Li, Pablo Arboleda]